Empirical Likelihood-Based Inference in Conditional Moment Restriction Models Export

@article{kitamura:tripathi:ahn:2004, abstract = {This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples.}, author = {Kitamura, Yuichi and Tripathi, Gautam and Ahn, Hyungtaik}, citeulike-article-id = {5097991}, citeulike-linkout-0 = {http://dx.doi.org/10.2307/3598764}, citeulike-linkout-1 = {http://www.jstor.org/stable/3598764}, doi = {10.2307/3598764}, issn = {00129682}, journal = {Econometrica}, keywords = {asymptotics, econometric\_theory, empirical\_likelihood, instrumental\_variables}, number = {6}, pages = {1667--1714}, posted-at = {2009-07-09 19:25:35}, priority = {2}, publisher = {The Econometric Society}, title = {Empirical Likelihood-Based Inference in Conditional Moment Restriction Models}, url = {http://dx.doi.org/10.2307/3598764}, volume = {72}, year = {2004} }

TY - JOUR ID - kitamura:tripathi:ahn:2004 L3 - citeulike-article-id:5097991 N2 - This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples. IS - 6 JF - Econometrica SN - 00129682 EP - 1714 TI - Empirical Likelihood-Based Inference in Conditional Moment Restriction Models PB - The Econometric Society VL - 72 SP - 1667 KW - asymptotics KW - econometric_theory KW - empirical_likelihood KW - instrumental_variables AU - Kitamura, Yuichi AU - Tripathi, Gautam AU - Ahn, Hyungtaik PY - 2004/// UR - http://dx.doi.org/10.2307/3598764 ER -